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高分辨率网格化的印度季风区土壤湿度和土壤温度数据集。

High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region.

机构信息

School of Earth Ocean and Climate Sciences, Indian Institute of Technology, Bhubaneswar, Odisha - 752050, India.

Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur- 721302, India.

出版信息

Sci Data. 2018 Nov 20;5:180264. doi: 10.1038/sdata.2018.264.

DOI:10.1038/sdata.2018.264
PMID:30457572
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6244185/
Abstract

High-resolution soil moisture/temperature (SM/ST) are critical components of the growing demand for fine-scale products over the Indian monsoon region (IMR) which has diverse land-surface characteristics. This demand is fueled by findings that improved representation of land-state help improve rainfall/flood prediction. Here we report on the development of a high-resolution (4 km and 3 hourly) SM/ST product for 2001-2014 during Indian monsoon seasons (June-September). First, the quality of atmospheric fields from five reanalysis sources was examined to identify realistic forcing to a land data assimilation system (LDAS). The evaluation of developed SM/ST against observations highlighted the importance of quality forcing fields. There is a significant relation between the forcing error and the errors in the SM/ST. A combination of forcing fields was used to develop 14-years of SM/ST data. This dataset captured inter-annual, intra-seasonal, and diurnal variations under different monsoon conditions. When the mesoscale model was initialized using the SM/ST data, improved simulations of heavy rain events was evident, demonstrating the value of the data over IMR.

摘要

高分辨率土壤湿度/温度（SM/ST）是印度季风区（IMR）对精细化产品日益增长需求的关键组成部分，该地区具有多样的地表特征。这些需求的产生是因为人们发现，改进土地状态的表示有助于提高降雨/洪水的预测能力。在这里，我们报告了 2001-2014 年印度季风季节（6 月至 9 月）期间开发的高分辨率（4km 和 3 小时）SM/ST 产品。首先，检查了五个再分析资料来源的大气场质量，以确定对陆地数据同化系统（LDAS）的实际强迫。对开发的 SM/ST 与观测值的评估强调了高质量强迫场的重要性。强迫误差与 SM/ST 误差之间存在显著关系。利用强迫场组合来开发 14 年的 SM/ST 数据。该数据集捕捉了不同季风条件下的年际、季节内和日变化。当使用 SM/ST 数据初始化中尺度模型时，明显改善了大雨事件的模拟，证明了该数据在 IMR 的价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b905/6244185/1941990bdc34/sdata2018264-f8.jpg
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本文引用的文献

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Sci Rep. 2017 Jan 27;7:41377. doi: 10.1038/srep41377.
2
Agriculture intensifies soil moisture decline in Northern China.农业加剧了中国北方土壤湿度的下降。
Sci Rep. 2015 Jul 9;5:11261. doi: 10.1038/srep11261.
3
Regions of strong coupling between soil moisture and precipitation.土壤湿度与降水之间的强耦合区域。
Science. 2004 Aug 20;305(5687):1138-40. doi: 10.1126/science.1100217.